Remote sensing of biodiversity: Using neural networks to estimate the diversity and composition of a Bornean tropical rainforest from Landsat TM data


Foody, G.M. and Cutler, M.E. (2002) Remote sensing of biodiversity: Using neural networks to estimate the diversity and composition of a Bornean tropical rainforest from Landsat TM data. In, Papers in proceedings of the IGARSS '02 conference. Geoscience and Remote Sensing Symposium, IGARSS 2002 IEEE International Piscataway, N.J., USA, IEEE, 497-499. (doi: 10.1109/IGARSS.2002.1025085).

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Description/Abstract

Two types of neural network were used to derive measures of biodiversity from Landsat TM data of a tropical rainforest. A feedforward neural network was used to estimate species richness while a Kohonen neural network was used to provide information on species composition. The results indicate the potential of remote sensing as a source of maps of biodiversity.

Item Type: Book Section
Related URLs:
Subjects: G Geography. Anthropology. Recreation > G Geography (General)
Divisions: University Structure - Pre August 2011 > School of Geography > Remote Sensing and Spatial Analysis
ePrint ID: 15214
Date Deposited: 30 Mar 2005
Last Modified: 27 Mar 2014 18:05
URI: http://eprints.soton.ac.uk/id/eprint/15214

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